Staff publications (AEPe)

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Browsing Staff publications (AEPe) by Subject "4010 Engineering Practice and Education"

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    Evaluation of an intuitive 4WD drift assist control concept in a driving simulator
    (Taylor & Francis, 2025-12-31) Sun, Yiwen; Velenis, Efstathios; Krishnakumar, Ajinkya
    In this paper, we present a concept of drift assist control for a 4-Wheel-Drive (4WD) electric vehicle that allows independent wheel torque control, aiming at an intuitive interaction with the average human driver. The concept is evaluated through a driver-in-loop trial using a driving simulator. Starting with a 4WD drift equilibrium analysis, we demonstrate the necessity of incorporating the throttle input for sideslip control and the idea of restricting the sideslip rate in order to assist the driver in stabilising the vehicle in drifting. Subsequently, we design a sideslip rate and yaw rate controller according to the desired sideslip angle from the driver using torque vectoring. To evaluate our control concept, a circular track is built in Cranfield University’s driving simulator based on the IPG CarMaker software. 34 participants were recruited to perform two drifting tasks, including the transition from normal cornering to drifting and regulating the sideslip under different configurations of sideslip damping rate and steering wheel feedback torque. Through subjective questionnaires and objective evaluation of vehicle states, the results show that our concept can assist the driver in intuitively controlling the vehicle during drifting.
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    Optimising vehicle performance with advanced active aerodynamic systems
    (Taylor and Francis, 2025-01-01) Rijns, Steven; Teschner, Tom-Robin; Blackburn, Kim; Siampis, Efstathios; Brighton, James
    This study investigates the performance potential of advanced active aerodynamic systems on high-performance vehicles. Static and active aerodynamic configurations, including asymmetrically actuated systems, are evaluated to identify performance gains and the mechanisms driving these improvements. Vehicle performance is optimised using a minimum lap time simulation framework, which utilises a transient vehicle dynamics model and CFD-derived aerodynamic data. Results indicate that configurations with greater aerodynamic adaptability enhance acceleration, braking, cornering, and straight-line performance, yielding notable lap time reductions compared to a static aerodynamic configuration. The asymmetrically controlled aerodynamic configuration achieves the highest lap time reduction of approximately 0.92 s (0.76%) due to its ability to modulate downforce both longitudinally and laterally. Optimal control strategies show that aerodynamic elements are actuated to balance vertical tyre load shifts resulting from load transfer, prioritising downforce on underloaded tyres in demanding scenarios like braking, cornering, and acceleration. Additionally, optimal design parameters for the brake, torque and roll stiffness distributions shift rearward as configurations provide greater control of aerodynamic loads on the rear axle. Overall, this research demonstrates the performance advantages of active aerodynamic systems and offers insights into the mechanisms underlying these enhancements, establishing a foundation for further innovations in the field.
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    Revolutionizing power electronics design through large language models: applications and future directions
    (Elsevier, 2025-04) Ibrahim, Khalifa Aliyu; Luk, Patrick Chi-Kwong; Luo, Zhenhua; Ng, Seng Yim; Harrison, Lee
    The design of electronic circuits is critical for a wide range of applications, from the electrification of transportation to the Internet of Things (IoT). It demands substantial resources, is time-intensive, and can be highly intricate. Current design methods often lead to inefficiencies, prolonged design cycles, and susceptibility to human error. Advancements in artificial intelligence (AI) play a crucial role in power electronics design by increasing efficiency, promoting automation, and enhancing sustainability of electrical systems. Research has demonstrated the applications of AI in power electronics to enhance system performance, optimization, and control strategy using machine learning, fuzzy logic, expert systems, and metaheuristic methods. However, a review that includes the recent AI advancements and potential of large language models (LLMs) like generative pre-train transformers (GPT) has not been reported. This paper presents an overview of applications of AI in power electronics (PE) including the potential of LLMs. The influence of LLMs-AI on the design process of PE and future research directions is also highlighted. The development of advanced AI algorithms such as pre-train transformers, real-time implementations, interdisciplinary collaboration, and data-driven approaches are also discussed. The proposed LLMs-AI is used to design parameters of high-frequency wireless power transfer (HFWPT) using MATLAB as a first case study, and high-frequency alternating current (HFAC) inverter using PSIM as a second case study. The proposed LLM-AI driven design is verified based on a similar design reported in the literature and Wilcoxon signed-rank test was conducted to further validate the result. Results show that the LLM-AI driven design based on the OpenAI foundation model has the potential to streamline the design process of power electronics. These findings provide a good reference on the feasibility of LLMs-AI on power electronic design.